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ONDL: An optimized Neutrosophic Deep Learning model for classifying waste for sustainability.

Nour Eldeen Mahmoud Khalifa1, Mohamed Hamed N Taha1, Heba M Khalil2

  • 1Information Technology Department, Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt.

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|November 8, 2024
PubMed
Summary

An Optimized Neutrosophic Deep Learning (ONDL) model effectively classifies waste using computer vision. This AI approach enhances waste management for a greener planet.

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Area of Science:

  • Environmental Science
  • Computer Science
  • Artificial Intelligence

Background:

  • Sustainability is crucial for a greener planet, with effective waste classification and management playing a vital role.
  • Computer algorithms and deep learning offer advanced solutions for waste management challenges.

Purpose of the Study:

  • To propose an Optimized Neutrosophic Deep Learning (ONDL) model for accurate waste object classification.
  • To evaluate the ONDL model's performance on two distinct waste datasets (DSWM1 and DSWM2).

Main Methods:

  • The ONDL model utilizes Deep Transfer Learning (DTL) based on Alexnet, incorporating True (T) neutrosophic domain conversion.
  • Grey Wolf Optimization (GWO) is employed for efficient image feature selection within the ONDL architecture.
  • Comparative analysis involved testing various DTL models (Alexnet, Googlenet, Resnet18) and neutrosophic domains (T, I, F).

Main Results:

  • The ONDL model demonstrated superior efficiency compared to other tested models.
  • On DSWM1 (2 classes), ONDL achieved Testing Accuracy (TA) of 0.9189, Precision (P) of 0.9177, Recall (R) of 0.9176, and F1 score of 0.9177.
  • On DSWM2 (3 classes), ONDL achieved TA of 0.8532, P of 0.7728, R of 0.7944, and F1 score of 0.7835, showing competitive results.

Conclusions:

  • The ONDL model is an effective deep learning approach for waste classification.
  • The proposed model achieves competitive performance metrics, contributing to improved waste management sustainability.
  • The study highlights the potential of neutrosophic deep learning in addressing environmental challenges.